Quantifying COVID-19 enforced global changes in

Preprint accepted in Remote Sensing Applications: Society and Environment 1

Quantifying COVID-19 enforced global changes in atmospheric

pollutants using cloud computing based remote sensing

Manmeet Singh1,2, Bhupendra Bahadur Singh1,3, Raunaq Singh4, Badimela Upendra5, Rupinder

Kaur6, Sukhpal Singh Gill7, Mriganka Sekhar Biswas*1,8

[email protected], [email protected], [email protected],

[email protected], [email protected], [email protected],

[email protected]

1 Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune, India, Ministry of

Earth Sciences, Government of India

2 IDP in Climate Studies, Indian Institute of Technology, Bombay, India

3 Department of Geophysics, Banaras Hindu University, Varanasi, India

4 School of Sciences, Indira Gandhi National Open University, Delhi, India

5 National Centre for Earth Science Studies, Thiruvananthapuram, India, Ministry of Earth Sciences,

Government of India

6 Department of Chemistry, Guru Nanak Dev University, Amritsar, India

7 School of Electronic Engineering and Computer Science, Queen Mary University of London, London,

United Kingdom

8 Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India

*Corresponding Author

Mriganka Sekhar Biswas Email: [email protected] Centre for Climate Change Research Indian Institute of Tropical Meteorology Pashan, Pune 411008, India

mailto:[email protected]









Abstract

Global lockdowns in response to the COVID-19 pandemic have led to changes in the

anthropogenic activities resulting in perceivable air quality improvements. Although several recent

studies have analyzed these changes over different regions of the globe, these analyses have

been constrained due to the usage of station based data which is mostly limited upto the

metropolitan cities. Also the quantifiable changes have been reported only for the developed and

developing regions leaving the poor economies (e.g. Africa) due to the shortage of in-situ data.

Using a comprehensive set of high spatiotemporal resolution satellites and merged products of

air pollutants, we analyze the air quality across the globe and quantify the improvement resulting

from the suppressed anthropogenic activity during the lockdowns. In particular, we focus on

megacities, capitals and cities with high standards of living to make the quantitative assessment.

Our results offer valuable insights into the spatial distribution of changes in the air pollutants due

to COVID-19 enforced lockdowns. Statistically significant reductions are observed over

megacities with mean reduction by 19.74%, 7.38% and 49.9% in nitrogen dioxide (NO2), aerosol

optical depth (AOD) and PM2.5 concentrations. Google Earth Engine empowered cloud computing

based remote sensing is used and the results provide a testbed for climate sensitivity experiments

and validation of chemistry-climate models. Additionally, Google Earth Engine based apps have

been developed to visualize the changes in a real-time fashion.

Keywords

COVID19, Google Earth Engine, PM2.5, NO2, AOD, Tropospheric ozone, Cloud Computing


1. Introduction

The impedance caused by the COVID-19 pandemic has led to worldwide disruptions in day-to-

day human activities across the globe. As per the World Health Organization (WHO) Weekly

Epidemiological Update issued on 15th December 2020, more than 70 million were infected

alongside death numbers exceeding 1.6 million worldwide. The characteristics of the virus

included rapid spread from human-to-human making its infections difficult to contain. There has

been an evidence of virus spreading through the air, after the WHO declared it a pandemic on

30th January 2020 (Zander et al, 2020). Almost every country was affected with frequent cases of

infected but asymptomatic individual. Those potential virus carriers made its transmission hard to

track. In that scenario and unavailability of any vaccine hitherto, most of the countries declared

lockdowns to prevent the spread of novel coronavirus. This led to a halt in the anthropogenic

activities in urban and industrialized areas across the world.

Estimates from the United Nations show that 55% of the world’s population lives in urban

areas as of 2018, and by 2050 close to 68% would be living in urban areas (The Department of

Economic and Social Affairs, 2019). By 2018, there are 33 megacities in the world with population

more than 10 million, hosting ~12.5% of the world’s total urban population. Due to the high

population density, understanding the environmental impacts of megacities are of utmost concern

for policy makers to ensure public health and safety. Emissions from the industries, automobiles,

construction processes, and other anthropogenic activities have led to high levels of air pollution

over megacities (Butler et al., 2008; Gurjar et al., 2008; Molina et al., 2012; Baklanov et al., 2016;

Marlier et al., 2016), making them hotspot for various greenhouse gases, ozone precursors and

aerosols observed well upto the tropopause layers (Brunamonti et al., 2018; Hanumanthu et al.,

2020). Various studies have reported increased mortality rates around the world’s megacities

caused by the air pollution. Cohen et al. (2017) reported that exposure to PM2.5 and tropospheric

ozone (O3) caused ~4·2 and ~0.25 million deaths worldwide respectively in 2015. Gaseous


pollutants like nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) are also

responsible for human health hazards (WHO, 2013; US Environmental Protection Agency, 2015).

The primary sources of nitrogen dioxide in the atmosphere are fossil fuel and biomass burning

and various microbiological reactions in wildfires, lightning and soils. In addition to the

anthropogenic sources, natural emissions such as that from the volcanic eruptions are also

harmful to the environment in the short term and also modulating the global hydroclimate (Singh

et al. 2020).

Nitric oxide (NO) rapidly oxidizes to NO2 in the air and plays crucial role in the formation

of photochemical smog, affects air quality and climate. The dominant impact of NOx (NO+NO2)

emissions on the climate is through the formation of O3, the third largest single contributor to

positive radiative forcing. Emissions of NOx generate indirect negative radiative forcing by

shortening the atmospheric lifetime of CH4. NOx dominantly controls the O3 budget through

photochemistry. It is well documented that high amounts of NOx emitted from the industries,

thermal power plants and automobiles leads to surface ozone production (Lelieveld et al., 2000)

and leads to the exceedances of the standard limit of the urban ozone concentration (Frost et al.,

2006). Tropospheric NO and O3 are potential greenhouse gases and influence the lifetime of the

other greenhouse gases. Also, the NOx compounds act as precursors for the aerosol nitrate and

influences significantly the abundance of the hydroxyl radical (OH). Furthermore, NO2 also

produces nitric acid (HNO3) which is a major component of acid rain by reacting with hydroxyl

radical (OH). The sources of NOx include both natural and anthropogenic sources. The natural

sources include atmospheric flux exchanges, lighting activity, soil emissions, and forest and

grassland fires while the anthropogenic sources are primarily through emissions from the power

plants, transportation (automobiles, ships and aircrafts), industrial emissions and biomass burning

(Guha et al., 2020). The lifetime of NOx is of the order of minutes to hours, which depends on

various factors such as the season, location, photolysis rate and the concentration of hydroxyl


radical (Lamsal et al., 2010). Hence, the short life time and the inhomogeneous source distribution

of NOx clearly lead to the spatiotemporal variations in the NO2 concentration in the troposphere

and it is well proven that tropospheric NO2 observed from the space is dominated by the amount

in the boundary layer (Ghude et al., 2008). Total global NOx emissions have increased from an

estimated pre-industrial value of 12 TgN yr–1 (Holland et al., 1999; Galloway et al., 2004) to

between 42 and 47 TgN yr–1 in 2000 (Solomon et al., 2007). The range of surface NOx emissions

(excluding lightning and aircraft) used in the current generation of global models is 33 - 45 TgN

yr–1 with small ranges for individual sources.

Satellite observations in the past few decades have helped to study long-term spatial and

temporal variation of pollutants around the world. Hilboll et al. (2013) and Georgoulias et al. (2019)

studied the long-term trend of NO2 over the world's megacities using multiple satellite

observations. Long term trends of SO2 have also been studied across the world where the Asian

countries show mostly increasing trends whereas opposite trends have been reported over North

America in the recent decades (Lu et al., 2011, 2013; Kharol et al., 2017; van der A et al., 2017).

Using OMI/MLS satellite observation Cooper et al. (2020) reported 8% relative increase in

tropospheric ozone burden over northern hemisphere (NH) compared to southern hemisphere

(SH) in recent times. The analysis of these satellite datasets and their pre-processing is

cumbersome and error prone due to involved steps. Raw satellite datasets are available in

different formats for different satellites and are mostly available as swath-based products in case

of missions such as MODIS and image blocks in case of Sentinel. The analysis of these datasets

requires them to be mapped to gridded form, necessitating the need of transforming to real

coordinates which is a challenging task. Moreover, different satellites-based instruments require

different corrections and filters to be applied based on data quality over different regions and

instrument-to-instrument differences. The requirement of large disk-space is also a challenge for

many researchers, due to unavailability of high-end computational resources at their end.


Cloud computing offers hope in solving these challenges by providing cloud as a service

platform for preprocessing, analyzing and visualizing big data (Gill et al., 2019). Google has

introduced a new cloud-based platform called Google Earth Engine for efficient and fast

processing of large geospatial datasets. It provides a systematic platform to analyze planetary-

scale geospatial data to uncover robust computational capacities of Google which can be used

as a source for analyzing problems and proposing solutions for environmental protection, climate

monitoring, water management, food security, disease, disaster, drought and deforestation.

Moreover, this engine is now being utilized to distribute and share results with others, to develop

mobile apps or web based services, interpreting various types of geospatial data, and for

assessing land use change, monitoring climate, malaria risk mapping, flood mapping, urban

mapping, rice paddy mapping, crop yield estimation, global surface water change and global

forest change. For example, an application based on Google Earth Engine is developed to detect

land cover change, which has been implemented successfully in Singapore (Sidhu et al., 2018).

Researchers from Ukraine used Google Earth Engine and created a high resolution crop

classification map for a large spatial region (Shelestov et al., 2017). Further, it has been identified

that machine learning techniques such as random forest and linear regression are working

efficiently for satellite imagery processing and COVID-19 predictions (Tamiminia et al., 2020; Tuli

et al., 2020).

Numerous recent studies have reported improvement in air quality around the world due

to large scale lockdown as a preventive measure to contain COVID-19. Muhammad et al. (2020)

reported such an improvement using satellite data, however only over a short span of time i.e.,

the initial lockdown period. He et al. (2020) studied the short-term impacts of lockdowns across

cities in China by analyzing air quality parameters such as PM2.5, PM10, NO2, O3, CO and SO2

using station based data. Their findings showed that large and wealthier cities in China had a

greater reduction in air pollution than otherwise. Huang et al. (2020) showed that impact of


COVID-19 forced lockdowns in improving air quality was not always apparent. Shen et al. (2020)

studied the changes in air pollution with respect to meteorology by comparing the 2020 lockdown

period with the 21-year long term means. They noted that large-scale transport of the pollutants

reflected importance of meteorology on air quality at regional scales. Similarly, Chang et al. (2020)

attributed enhanced haze creation during lockdown in China to pollutant pathways. Rodríguez-

Urrego and Rodríguez-Urrego (2020) reported reduction in PM2.5 over 50 most polluted capitals

around the world. They found a 12% reduction in PM2.5 around the world's most polluted capitals

with Bogotá, Colombia showing the highest decrease (57%). Sharma et al. (2020) reported

reduction in air pollution in 22 Indian cities during COVID-19 forced lockdown. Similarly,

improvement in air quality over central China during lockdown was also reported by Xu et al.

(2020). Berman and Ebisu (2020) reported a reduction in NO2 and PM2.5 predominantly over the

urban United States. They compared early and late/no business closure scenarios in different

counties of the country and found clear reductions in the pollutant levels in early closure scenario

over the urban counties. Li et al. (2020) used the WRF-CAMX model to assess lockdown induced

changes in air quality over Yangtze river delta. They found that though the daily PM2.5 reduced

during the lockdown, it was still high and more stringent measures were required for better air

quality. Menut et al. (2020) also employed WRF-CHIMERE modelling system for understanding

the pandemic enforced lockdown changes in air quality over the western Europe during March

2020. Using satellite observations Biswas and Ayantika (2020) reported decrease in NO2,

formaldehyde (HCHO), SO2 and Aerosol Optical Depth (AOD) over India during pre-monsoon

period (March, April and May) compared to past three years due to COVID-19 induced lockdown.

The study reported that mitigation of ozone and large reductions in NO2 were associated with the

muted decrease in particulate matter concentrations. However, our survey finds some limitations

in these studies. Firstly, most of them have used station data to assess the changes in local and

regional air quality which limits the spatial coherence and continuity, particularly the usage of

PM2.5 has been limited in most of the recent studies. Also, there has been an absence of


discussions about the AOD, which provides information about the amount of direct sunlight

blocked by various particles/gases when it reaches the surface of Earth. Secondly, the previous

analyses have reported the cases from brief lockdowns during the early phase of pandemic

thereby limiting the robustness of the conclusion. Thirdly, the published results have mostly

discussed the air quality changes in developed and developing city/regions while the relatively

poorer regions (e.g. African continental regions) have mostly been ignored. In addition to these

factors, the existing works are limited by the spread of the observational networks out of the

country capitals or even limited observations in the major cities. For example, the existing station

data network of Central Pollution Control Board which has been used for the past studies over

India, has a good coverage only over the national capital region around Delhi.

Given its global presence, almost every country in the world has used lockdowns as a

preventive measure to contain the further spread of pandemic. As seen in the literature survey,

qualitatively, it has been felt that the lockdowns have improved air quality due to a reduction in

emissions arising out of suppressed anthropogenic activities. Various studies have used station

based datasets to bring out this change whereas others have used satellite datasets to show the

improvements in air quality over a limited period of the lockdown. Moreover, the studies have

been regional in nature and have also not taken into account the high-resolution panoply of

datasets into consideration. Having noted the limitations in the recent studies, the present study

utilizes the Google Earth Engine’s capabilities to quantify the changes in atmospheric pollutants

such as NO2, AOD, O3 and PM2.5 during the COVID forced lockdowns globally. We use multi-

satellite high resolution datasets spanned over the complete lockdown period until 31 May 2020

to show its environmental affects over 8 major continental parts of the world. Cloud computing

based remote sensing via Google Earth Engine is used and lesser reported regions in other

studies such as Africa have been included and thoroughly explored. Additionally, we also provide


Google Earth Engine based apps to visualize the changes in air quality over city-scale spatial

resolutions.

2. Data and Methodology

The Google Earth Engine, which is the first ever cloud computing based platform enabling

processing, analysis and visualization of satellite and other datasets for the planet Earth, is

extensively used in this study. Google Earth Engine enables the processing, analyzing and

visualization of these large datasets on the cloud. A large number of the datasets being made

available after quality control. Hence, there is no need of performing any major preprocessing or

storage making the analysis a smooth process. This study employs Google Earth Engine

extensively for the analysis and visualization of air quality and other datasets. In this work it has

served as a nodal point for targeted analysis of the quantitative environmental assessment of

COVID-19 enforced lockdowns. The analysis is enabled by direct API calls to the requisite data

and there is no need to download the same. Its use has catapulted the large-data processing

which is otherwise very tedious and error prone task using the traditional approaches. The access

to the platform is provided by an application process and the APIs are available in javascript and

python. In this work, the python API has been used for analysis. All the codes for analysis and

visualization are available at https://github.com/manmeet3591/gee_lockdown.

Satellite data products viz NO2 and tropospheric O3 are used from TROPOspheric

Monitoring Instrument (TROPOMI; Veefkind et al., 2012), an instrument onboard Sentinel-5

precursor (Sentinel-5P) satellite. The AOD is obtained from MODIS (Schaaf et al., 2002) and

particulate matter less than 2.5μ (PM2.5) are also based on the data assimilated MODIS product.

The European Space Agency had launched Sentinel-5P on 13 October 2017 as a dedicated

satellite to observe air pollution. The datasets are available in two versions i.e. Offline (OFFL) and

Near Real-Time (NRTI). NRTI products are available earlier than the OFFL products, however

https://github.com/manmeet3591/gee_lockdown


OFFL products offer better quality than NRTI and hence are used in this work. The datasets from

Sentinel-5P and MODIS provide daily measurements if the sky is not cloudy. The data from

TROPOMI is accessed using Google Earth Engine and is available from July 2018. Tropospheric

ozone concentrations available for the tropical band 20oS-20oN is archived at Google Earth

Engine servers using the raw data and cloud slicing (csa) and convective cloud differential (ccd)

algorithms. AOD data is used from the version 6 of combined MODIS Terra and Aqua product

wherein atmospheric corrected data over land at 1 km horizontal resolution is provided by Google

Earth Engine. PM2.5 is used from the Copernicus Atmosphere Monitoring Service (CAMS) Global

Near-Real-Time accessed using Google Earth Engine after 4D-Var data assimilation using

datasets from MODIS. 4D-Var is an advanced analytical method to perform data assimilation.

Data assimilation involves combining short-range prediction with in-situ measurements to provide

the best approximation of Earth system. COVID-19 lockdown start dates are accessed from the

news reports and popular articles. Since lockdowns were still on in countries, the present study

considers the period upto 31 May 2020 (Table 1).

We calculate the percentage change of various air quality parameters, which is the

percentage difference of aggregated means during the two periods. The two periods are defined

as the lockdown period for year 2020 (Y20) and the corresponding epoch in the year 2019 (Y19).

For instance, to calculate the percentage change in NO2 over South Asia, we average the NO2

maps over Y19 and Y20 and then obtain [(Y19 - Y20)/(Y19)]*100 as the percentage change in

NO2 over South Asia in Y19 relative to Y20. We also select 93 urban cities globally by first

selecting the megacities (Mage et al., 1996; Gurjar et al., 2010, 2016; Baklanov et al., 2016;

Cheng et al., 2016; Marlier et al., 2016), followed by European Union capitals and then the cities

with GDP per capita greater than $ 25000. The changes in air quality parameters over these cities

are also performed by the methodology above and averaging over the area of the city. The latitude

longitude information of the cities is obtained from www.latlong.net. We define the extent of the

http://www.latlong.net/


cities by the area of the cities from these central points (latitude, longitude) which is also taken as

the region over which we compute the pollutant concentration variations. It is worthy to note that

for some countries such as Japan and South Korea which did not enforce lockdowns, the analysis

for the cities is not done to bring out the exact impact of lockdowns over the air quality. The

coordinates, population and GDP data of the global megacities along with the start and end of the

lockdown enforcement dates are available on https://rb.gy/t7jzr8. Interactive maps of

meteorological fields such as land surface temperature and surface winds along with four air

pollutants showing the absolute values of COVID19 lockdown period in 2020 and the

corresponding period from 2019 have been prepared as applications of Google Earth Engine.

The links for the same can be found from the section 3.9.

3. Results and Discussion

In this section we discuss the environmental changes in 8 major regions of the world due to

COVID-19 enforced lockdowns. The subsections describe the changes over Africa, Australia

and New Zealand, East Asia, South Asia, Europe, North America, South America, Southeast

Asia and the global megacities.

3.1 Africa

Surface station-based observations records in various African regions show annual mean NO2

and O3 concentrations to be in the range of 0.9-2.4 ppb and 4.0-14.0 ppb respectively (Adon et

al. 2010). However, during the lockdown period, the level of these pollutants reduced significantly

over the region. The reduction in NO2 concentrations (Fig. 1a) are observed as blobs over urban

areas with more than ~30% decrease over large swathes of South Africa, Botswana, Namibia,

Angola, Tanzania, Kenya and coastal countries of West Africa. Algeria and Niger show a

decrease by ~20% in NO2 concentrations. Over the Arabian Peninsula the decrease is seen only

over urban areas such as Riyadh, Dubai, Muscat, Bahrain, Qatar and Israel. The tropospheric

https://rb.gy/t7jzr8


ozone product is available only from 20oS to 20oN and a 20-30% decrease can be seen over

regions surrounding Tanzania, Zambia, Angola, Kenya and Congo (Fig. 1c). The African regions

usually have high AOD values during the local dry seasons (Boiyo et al., 2016) coinciding with

the reduced human activity. This period also overlaps with the lockdown periods (March-May)

used in the study. However, we observe a decrease in the AOD (Fig. 1b) over South Africa,

Botswana and Angola in Southern Africa by ~30-50%, over Nigeria, Ghana Cote d’Ivoire, Sierra

Leone and parts of Niger in Eastern Africa by ~30-50%, over Sudan and Egypt by ~20% and parts

of north Libya by more than 50% relative to the same period (as that of lockdown period in 2020)

in 2019. In the Middle-East, parts of Saudi Arabia, Iraq and Iran show a decrease by ~30%. In

addition, parts of Congo, Tanzania and Kenya also show reduction in AOD when compared with

the month before lockdown. PM2.5 concentrations across African regions have different

characteristics wherein West Africa has higher levels partly arising from dust, while the rest of

Africa has anthropogenic factors dominating the overall concentrations (Heft-Neal et al. 2018).

During the analysis period, reduction in PM2.5 levels can be observed over large parts of Namibia,

western South Africa, the entire Arabian Peninsula and North Africa to negligible amounts during

lockdown (Fig. 1d). A two-thirds decrease can be seen over other parts of Africa. The relative

decrease of ~30% in NO2 over Africa is indeed significant. However, when absolute values are

analyzed, for example using Google Earth Engine apps, we can observe significant changes only

over South Africa and parts of Namibia, Tanzania and Kenya. Over Angola, Namibia and South

Africa decrease in NO2 concentration and in PM2.5 are almost anti-correlated. We observe that

over some regions of Africa, although the change might seem to be large, the base value is not

substantial, which can be clearly understood from the Google Earth Engine apps.

3.2 Australia and New Zealand

The regions in Australia and New Zealand have strong seasonal cycles in the atmospheric

pollutants’ levels. The forest fire/burning lead to enhanced levels apart from the contribution from


human activities (Reisen et al. 2012). The local autumn season coincided with the analysis period

wherein NO2 concentration reductions by ~30% can be seen over urban areas such as

Melbourne, Sydney, Auckland, large swathes of New South Wales, Canterbury, Otago and

Southland (Fig. 2a). The region does not show much change in AOD except for areas in Brisbane,

Manawatu-Wanganui and neighborhoods of Perth and Auckland (Fig. 2b). The tropospheric

ozone data is available only for the Northern part of Australia and a reduction by 15-20% can be

seen throughout the region (Fig. 2c). PM2.5 shows large reductions (upto 100% in many regions)

over large parts of Australia and New Zealand. Eastern Australia shows two-third reductions in

PM2.5 concentrations (Fig. 2d). The decrease in NO2 concentration and PM2.5 are almost anti-

correlated over Australia and New Zealand. We observe a significant increase in AOD over

western and south-western Australia in 2020 which is due to the forest fires in the end of 2019

and the beginning of 2020. However, the signal is not observed in PM2.5 which requires further

investigation.


3.3 East Asia

Air pollution has increased over east Asia during the last few decades. The rise has come amidst

the industrial growth leading to higher emissions mostly from the regions in China and Korea (Kim

et al. 2011; Wang et al. 2017). The concentrations of air pollutants have increasing interannual

trend and summer season show increased intra-seasonal variations (Jacob and Winner, 2009).

Here in our study we note that NO2 concentrations show a high reduction of ~50% in the urban

areas of China with the neighborhoods characterizing a reduction of ~33%. South Korea, North

Korea and Japan show ~30-40% decrease in NO2 levels (Fig. 3a). It can be seen that AOD

reduced by ~50% over Shanghai, parts of South Korea, Beijing and regions around Xi'an. The

regions around these centers show ~33% reduction in AOD (Fig. 3b). Tropospheric ozone data

is not available (Fig. 3c) from the satellite over this region so we skip that analysis. PM2.5 shows

a reduction by ~33% over most of the parts of China, North Korea, South Korea and Japan with

more than 50% decrease over Mongolia and the surrounding regions (Fig. 3d). It is to be noted

that the color scale in Figure 3a-d for relative change is from -50% to +50%. Mongolia shows a

significant relative decrease in NO2 which is however not much valuable considering very low

base values, and western China show a significant decrease of PM2.5. Overall, the lockdown

effects in the air pollution are pronounced over east Asia which resulted in improvement of the air

quality.

3.4 South Asia

Similar to the east Asian region, the air pollution in south Asia has also gone up in the last few

decades (Mahajan et al., 2015; Fadnavis et al., 2020 and references therein). The region is

characterized by several developing economies that have paced up the industry thereby

increasing the air pollutants. The region has strong seasonal variability of air pollutants dominated

by the regional meteorological factors (Tiwari et al. 2013; Wang et al. 2017). Particularly the PM2.5


concentration minima are noticed in monsoon season which rises during the winter time and is

maintained until pre-monsoon. Surface pollutants over the region are dominated by the changes

over Indian subcontinent. The COVID-19 spread and subsequent lockdowns in 2020 were

imposed during the summer and pre-monsoon months. The changes in NO2 levels show

reductions by more than ~30% seen all over Tier I and II cities in India which are concentrated

over these urban areas (Fig. 4a). The neighboring countries also show similar characteristics with

Lahore, Islamabad, Karachi and Dhaka seeing around ~30% reductions in NO2 concentrations.

AOD reductions can only be observed over South India, Indo-Gangetic plains, West Bengal and

Myanmar by ~30-40% (Fig. 4b). For the limited region in South India over which Tropospheric

ozone data is available, we do not see any change in the concentrations (Fig. 4c). A remarkable

change is noticed in PM2.5 concentrations where reductions by ~60-70% are noted over large

swathes of South Asia with the reductions nearing complete decimation of the species over

western Rajasthan in India (Fig. 4d).

3.5 Europe

Past studies have shown that the air pollutants have distinct regional variability across Europe. In

general NO2, NOx and PM2.5 concentrations are found to be higher in Southern Europe while

lesser values are found over the regions in Western and Northern Europe (Eeftens et al. 2012).

The region is dominated by the street/urban background concentration ratios for PM2.5 along with

non-tailpipe emissions. Though meteorology plays an important role in the air pollutants variability

over Europe, this region has registered decreasing trends in most of the anthropogenic induced

pollutants due to air pollution controls (Barmpadimos et al. 2012; Wang et al. 2017). With specific

measures to curb the emissions, long term observations show that AOD has decreased over

Europe with largest AOD variations occurring during winter and spring followed by some

reductions in the summer and autumn months (Chiacchio et al. 2011). During the analysis period.

We notice reductions by ~30% in the major cities of Europe such as Lisbon, Madrid, Barcelona,


Toulouse, Monaco, Manchester, Birmingham, Istanbul, Moscow, Stockholm, Oslo, Helsinki and

large parts of Germany, regions in and surrounding Paris and London (Fig. 5a). Lowered AOD

values can be seen over cities of western Europe by ~20% and over eastern Europe by 30-50%

(Fig. 5b). The tropospheric ozone data is not available over Europe (Fig. 5c). PM2.5 concentrations

show a reduction by ~33% throughout western Europe except Switzerland and by ~60-70% over

eastern Europe, Sweden, Norway and Finland (Fig. 5d). However, a point to note is the miniscule

absolute reductions and the base values making relative changes over countries such as Norway

insignificant. We also see anomalous improvements in air quality over Switzerland are significant,

however only over major cities such as Zurich, Lucern and Basel. The baseline in other regions,

particularly the mountains is inherently low. However, looking at the absolute values from Google

Earth Engine apps, it seems that the lockdown was highly effective as the NO2 values can be

seen to have dropped down to near zero.

3.6 North America

The region was earlier a major source of emissions, however with strict emission regulations in

the last few decades there has been a marked reduction in rate of increment in air pollutants (NOx,

PM2.5) over the high-income regions of North America (Canada, United States) (Naghavi et al.

2015). Results from past studies show that AOD and PM2.5 agree in terms of interannual variability

and both have decreased with time. Strongest changes have been noted over the eastern part

with moderate changes over the central and western parts (Li et al. 2015). During the analysis

period, we notice further reduction in NO2 concentrations over North America which is visible in

patches hovering over and around the urban areas. The East coast of the United States of

America showed enhanced reductions in NO2 relative to the west coast. Prominent reductions

can be seen over New York, Atlanta, Charlotte, Detroit, Chicago, Denver, Los Angeles, San Jose,

Portland, Calgary, Edmonton, Toronto, Montreal and Mexico City by ~30% (Fig. 6a). Other

regions around these major centres of decreasing NO2 show ~20% decrease in NO2. Similar


characteristics can be seen in the change in AOD and PM2.5 concentrations (Fig. 6b,d).

Tropospheric ozone data is absent over North America in our analysis (Fig. 6c). We particularly

note a large increase in NO2 in Western Canada, much more extended than in the highly

populated Eastern side of the US. This is because the figure shows relative change, if however,

we compare the absolute values, it is much less as compared to Eastern US. The state of Alberta

and the city of Edmonton and its surroundings in particular have shown substantial improvements

in air quality even in terms of absolute values.

3.7 South America

The region is dominated by the seasonal variability in air pollutant levels where biomass burning

contributes maximum to the NOx variability (Castellanos et al. 2014). Concentrations of PM2.5 and

NOx in the urban parts are mostly influenced by the traffic being the main source (Krecl et al.,

2018). We note reduction in NO2 levels over large swathes of Brazil, Argentina, Chile, Peru,

Columbia and Venezuela by ~20-30% (Fig. 7a). A 33% decrease in AOD can be observed over

south-eastern Brazil, southern Argentina, Bolivia and Peru with the lowered values reaching 50%

concentrated in and around Sao Paulo (Fig. 7b). We can see 15-20% decrease in tropospheric

ozone concentrations over Brazil and Bolivia (Fig. 7c). Large parts of South America show PM2.5

reductions upto ~60-70% with some regions such as the Roraima in Brazil and Santa Cruz,

Chubut in Argentina showing complete removal of PM2.5 (Fig. 7d). An important point to note is

the improved air quality in South Argentina which is poorly populated but has considerable oil

production. We note as also from (https://www.spglobal.com/platts/en/market-insights/latest-

news/oil/062620-argentina-extends-tightens-lockdown-puts-fresh-damper-on-oil-demand-

production) that Argentina went into an extended period of lockdown and it also consumes most

of its oil. These might have been the reasons for improved air quality in those regions.

https://www.spglobal.com/platts/en/market-insights/latest-news/oil/062620-argentina-extends-tightens-lockdown-puts-fresh-damper-on-oil-demand-production




3.8 Southeast Asia

This region is also subjected to strong seasonal cycles in air pollutants due to changes in the

atmospheric circulation patterns, however the increasing emissions have a significant contribution

by the biomass burning from Peninsular Southeast Asia (Dong and Fu 2015; Wang et al.,

2017).The finer mode PM2.5 is mostly associated with human activities wherein the AODs peak

during the biomass burning season (Su et al., 2010; Lalitaporn et al., 2013). It can be seen that

during the lockdown period NO2 concentrations reduced by ~30% over Hanoi, Malaysia,

Singapore, Jakarta, Manila and large parts of Indonesia (Fig. 8a). Other regions in southeast Asia

also show a decrease by ~20% of NO2. Hanoi shows the maximum reduction in AOD by ~50%

followed by Singapore (~30-40%) and marginal decrease in other parts of southeast Asia (Fig.

8b). Tropospheric ozone decreased by 20-30% throughout the region (Fig. 8c) and PM2.5

decreased by ~60-70% uniformly over the area (Fig. 8d).

3.8 Megacities

More than half of the human population as of 2020 delves in urban areas. Past few decades have

seen exponential growth in the number of these agglomerates and larger urban segments known

as megacities. Megacities have large economies and are also sources of anthropogenic

pollutants. In recent times, the health hazards posed by these pollutants in the megacities have

become evident (Parrish et al., 2009). Urban areas contribute maximum to the rising emissions

throughout the world, with more pronounced effects in the developing countries. With the rise in

population and demands these regions are the hotspots of such air pollutants. In addition to the

regional analysis, we also dig into the lockdown forced changes in the megacities around the

world. We note a significant reduction in the pollutants considered in the study. The results are

present in the supplementary Table 2. We observe a statistically significant fall (Fig. 9) in the NO2,

AOD and PM2.5 concentrations (Table 2) with the reductions amounting to 19.74%, 7.38% and


49.9% as the mean with respect to the same period in 2019 as lockdowns in the respective

megacities in 2019. Fig. 9 shows the violin plots of the various species wherein the bubbles

represent the distribution of the global changes. We also note that the pandemic forced lockdown

led to limit the air pollution levels close to or below the targeted levels as prescribed by the WHO

and UNEP led air pollution monitoring network as part of the Global Environment Monitoring

System (Mage et al., 1996). Lima, Port Louis and Mumbai are the top three cities with regards to

reductions in NO2 concentrations ~72%, ~56% and ~50% during 2020 lockdown relative to 2019

whereas Bogota, Guangzhou and Shenzen showed nominally enhanced NO2 concentrations

during lockdown. Jakarta, Kuala Lumpur and Bangkok are the top three cities with regards to

2020 lockdown reductions in O3 concentrations to the tune of 16%, 12% and 10% relative to 2019,

whereas Panama City, Mexico City and Manila show enhanced O3 concentrations relative to

2019. Riyadh, Manama and Abu Dhabi show the largest reductions (75.6%, 73% and 72%

respectively) in PM2.5 concentrations during lockdown whereas Nassau is the only megacity

showing slightly enhanced (~17%) PM2.5 concentrations during lockdown. The megacities

Shenzen, Sao Paulo and Luxembourg show maximum decrease (55%, 48% and 46%

respectively) in AOD whereas Brussels, Panama City and Chongqing show an increase in AOD

during the lockdown relative to 2019.

3.9 Google Earth Engine apps

A set of six Google Earth Engine apps have been developed to aid in enhanced visualization of

the changes/improvements in air quality due to COVID-19 lockdowns. For best visualization of

these apps, the use of Google Chrome browser is recommended. They can be accessed from

the links below:

AOD: https://manmeet20singh15.users.earthengine.app/view/aodlockdown NO2: https://manmeet20singh15.users.earthengine.app/view/no2lockdown Tropospheric ozone: https://manmeet20singh15.users.earthengine.app/view/tropospherico3lockdown

https://manmeet20singh15.users.earthengine.app/view/aodlockdown

https://manmeet20singh15.users.earthengine.app/view/no2lockdown

https://manmeet20singh15.users.earthengine.app/view/tropospherico3lockdown


PM2.5: https://manmeet20singh15.users.earthengine.app/view/pm25lockdown Land surface temperature: https://manmeet20singh15.users.earthengine.app/view/lstlockdown Surface winds: https://manmeet20singh15.users.earthengine.app/view/windlockdown

4. Conclusions

In this study we have used remote sensing datasets accessed via Google Earth Engine to assess

and quantify the impact of COVID-19 enforced lockdowns on the air pollutants across the world

and over megacities in particular. This work has been possible only because of Google Earth

Engine and also to the open access SENTINEL-5P and MODIS data from the European Space

Agency and NASA. As compared to the air pollutants concentration for the same period in the

previous year, we find significant reductions over all regions in the parameters such as NO2, AOD,

tropospheric ozone and PM2.5. However, there are some anomalies or relative variations as well

as significant differences between NO2 and PM2.5 variations that could justify further work to

understand the reasons behind the variations. The satellite data are not available for 2018 and

previous years and hence the comparison has only been done relative to 2019. The comparison

of the COVID-19 enforced lockdown period to post-COVID-19 period would test the statistical

significance of our results which can be taken in a follow-up study. The main features of this study

are (i) use of complete lockdown period as compared to other works with limited time duration, (ii)

use of multi-pollutants datasets, (iii) use of spatially contiguous satellite datasets enabling better

understanding and (iv) understanding spatial distribution of the changes in air pollutants due to

lockdown. As seen from the analysis, the consistent variation over most big cities is a good

indication of the relationship between pollution decrease and COVID-19.

Further research can use the wind and temperature datasets to analyse the relationship. This

study gives equal importance to Africa, which is less covered by observational networks and

hence the information coming out of the region is limited (Dinku et al., 2019). The state-of-the-art

climate models have large uncertainties in simulating these air pollutants, and COVID-19 has

https://manmeet20singh15.users.earthengine.app/view/pm25lockdown

https://manmeet20singh15.users.earthengine.app/view/lstlockdown

https://manmeet20singh15.users.earthengine.app/view/windlockdown


provided a testbed for their validation. If we look at Angola, Namibia, South Africa, Australia and

New Zealand, the decrease in NO2 concentration and PM2.5 is almost anti-correlated. Since both

are only linked to human and particularly traffic reduction, further research needs to be carried

out on this aspect. Moreover, in some areas away from megacities large variations or anomalous

variations may not be significant if absolute values are low. This work can serve as a benchmark

to assess the climate model simulations understanding the role of lockdown on air quality and

hence can also be used to improve the climate model parameterizations.

5. Software Availability

The codes used in this study are available as an open source version from

https://github.com/manmeet3591/gee_lockdown

Fig. 1 COVID19 lockdown changes in atmospheric pollutants over Africa and Middle East:

Spatial maps of percentage change in concentrations of (a) NO2 (b) AOD (c) Tropospheric O3

and (d) PM2.5 for the 2020 COVID19 enforced lockdown period relative to the same period in

2019. The change in concentration is represented as (period in 2019 corresponding to the

lockdown in 2020 - 2020 COVID19 enforced lockdown) expressed in percentage relative to the

period in 2019 corresponding to the lockdown in 2020. The data used is from the TROPOMI

instrument onboard Sentinel-5P satellite, MODIS and Sentinel-2 MultiSpectral Instrument.


Fig. 2 COVID19 lockdown changes in atmospheric pollutants over Australia and New

Zealand: Spatial maps of percentage change in concentrations of (a) NO2 (b) AOD (c)

Tropospheric O3 and (d) PM2.5 for the 2020 COVID19 enforced lockdown period relative to the

same period in 2019. The change in concentration is represented as (period in 2019

corresponding to the lockdown in 2020 - 2020 COVID19 enforced lockdown) expressed in

percentage relative to the period in 2019 corresponding to the lockdown in 2020. The data used

is from the TROPOMI instrument onboard Sentinel-5P satellite, MODIS and Sentinel-2

MultiSpectral Instrument.


Fig. 3 COVID19 lockdown changes in atmospheric pollutants over East Asia: Spatial maps

of percentage change in concentrations of (a) NO2 (b) AOD (c) Tropospheric O3 and (d) PM2.5

for the 2020 COVID19 enforced lockdown period relative to the same period in 2019. The

change in concentration is represented as (period in 2019 corresponding to the lockdown in

2020 - 2020 COVID19 enforced lockdown) expressed in percentage relative to the period in

2019 corresponding to the lockdown in 2020. The data used is from the TROPOMI instrument

onboard Sentinel-5P satellite, MODIS and Sentinel-2 MultiSpectral Instrument. Note that the

color scale for relative change is from -50% to +50%.


Fig. 4 COVID19 lockdown changes in atmospheric pollutants over South Asia: Spatial

maps of percentage change in concentrations of (a) NO2 (b) AOD (c) Tropospheric O3 and (d)

PM2.5 for the 2020 COVID19 enforced lockdown period relative to the same period in 2019. The




onboard Sentinel-5P satellite, MODIS and Sentinel-2 MultiSpectral Instrument.


Fig. 5 COVID19 lockdown changes in atmospheric pollutants over Europe: Spatial maps of

percentage change in concentrations of (a) NO2 (b) AOD (c) Tropospheric O3 and (d) PM2.5 for

the 2020 COVID19 enforced lockdown period relative to the same period in 2019. The change

in concentration is represented as (period in 2019 corresponding to the lockdown in 2020 - 2020

COVID19 enforced lockdown) expressed in percentage relative to the period in 2019

corresponding to the lockdown in 2020. The data used is from the TROPOMI instrument



Fig. 6 COVID19 lockdown changes in atmospheric pollutants over North America: Spatial








Fig. 7 COVID19 lockdown changes in atmospheric pollutants over South America: Spatial








Fig. 8 COVID19 lockdown changes in atmospheric pollutants over Southeast Asia: Spatial








Fig. 9 Distributions of percentage change in NO2 concentrations, AOD, Tropospheric ozone and

PM2.5 of global megacities for the year 2019 relative to 2020


Table 1. Timelines of COVID19 enforced lockdowns in various regions of the world obtained

from news articles and national reports

Lockdown period Region

23 March 2020 to 31 May 2020 South Asia (India and surrounding regions)

23 March 2020 to 31 May 2020 South East Asia (Thailand, Malaysia,

Singapore and surrounding regions)

24 January 2020 to 25 March 2020 East Asia (China, Japan, Korea and

neighbourhoods)

23 March 2020 to 31 May 2020 Australia and New Zealand

13 March 2020 to 31 May 2020 Europe

23 March 2020 to 31 May 2020 North America

13 March 2020 to 31 May 2020 South America


30 March 2020 to 31 May 2020 Africa and Middle East

Table 2. Percentage change in NO2 concentrations, AOD, Tropospheric ozone and PM2.5 of

global megacities for the year 2019 relative to 2020. The first column shows species, the

second column represents the p-value after performing 1 sample t test to test the statistical

significance of change in air quality parameters. The null hypothesis is that the mean change in

percentage is 0. The third column is the mean percentage change for the year 2019 relative to

2020.

Species p-value Mean (% change)

NO2 0.0 19.74

AOD 0.004 7.38

Tropospheric O3 0.15 -3.23

PM2.5 0.0 49.9


References

Adon, M., Galy-Lacaux, C., Yoboué, V., Delon, C., Lacaux, J.P., Castera, P., Gardrat, E., Pienaar, J., Ourabi, H.A., Laouali, D., Diop, B., Sigha-Nkamdjou, L., Akpo, A., Tathy, J.P., Lavenu, F., Mougin, E., 2010. Long term measurements of sulfur dioxide, nitrogen dioxide, ammonia, nitric acid and ozone in Africa using passive samplers. Atmospheric Chemistry and Physics 10, 7467–7487. doi:10.5194/acp-10-7467-2010 Baklanov, A., Molina, L.T., Gauss, M., 2016. Megacities, air quality and climate. Atmospheric Environment 126, 235–249. doi:10.1016/j.atmosenv.2015.11.059 Baldasano, J.M., 2020. COVID-19 lockdown effects on air quality by NO2 in the cities of Barcelona and Madrid (Spain). Science of The Total Environment 741, 140353. doi:10.1016/j.scitotenv.2020.140353 Barmpadimos, I., Keller, J., Oderbolz, D., Hueglin, C., Prévôt, A.S.H., 2012. One decade of parallel PM10 and PM2.5 measurements in Europe: trends and variability. Atmospheric Chemistry and Physics Discussions 12, 1–43. doi:10.5194/acpd-12-1-2012 Berman, J.D., Ebisu, K., 2020. Changes in U.S. air pollution during the COVID-19 pandemic. Science of The Total Environment 739, 139864. doi:10.1016/j.scitotenv.2020.139864 Biswas, M.S., Ayantika, D.C. (2021). Impact of COVID-19 Control Measures on Trace Gases (NO2, HCHO and SO2) and Aerosols over India during Pre-monsoon of 2020. Aerosol Air Qual. Res. 21, 200306. https://doi.org/10.4209/aaqr.2020.06.0306 Boiyo, R., Kumar, K.R., Zhao, T., Bao, Y., 2017. Climatological analysis of aerosol optical properties over East Africa observed from space-borne sensors during 2001–2015. Atmospheric Environment 152, 298–313. doi:10.1016/j.atmosenv.2016.12.050 Brunamonti, S., Jorge, T., Oelsner, P., Hanumanthu, S., Singh, B. B., Kumar, K. R., Sonbawne, S., Meier, S., Singh, D., Wienhold, F. G., Luo, B. P., Boettcher, M., Poltera, Y., Jauhiainen, H., Kayastha, R., Karmacharya, J., Dirksen, R., Naja, M., Rex, M., Fadnavis, S., and Peter, T., 2018. Balloon-borne measurements of temperature, water vapor, ozone and aerosol backscatter on the southern slopes of the Himalayas during StratoClim 2016–2017, Atmos. Chem. Phys., 18, 15937–15957, https://doi.org/10.5194/acp-18-15937-2018 Butler, T., Lawrence, M., Gurjar, B., Aardenne, J.V., Schultz, M., Lelieveld, J., 2008. The representation of emissions from megacities in global emission inventories. Atmospheric Environment 42, 703–719. doi:10.1016/j.atmosenv.2007.09.060 Castellanos, P., Boersma, K.F., Werf, G.R.V.D., 2014. Satellite observations indicate substantial spatiotemporal variability in biomass burning NOx emission factors for South America. Atmospheric Chemistry and Physics 14, 3929–3943. doi:10.5194/acp-14-3929-2014 Chang, Y., Huang, R.J., Ge, X., Huang, X., Hu, J., Duan, Y., Zou, Z., Liu, X. and Lehmann, M.F., 2020. Puzzling haze events in China during the coronavirus (COVID‐19)

https://doi.org/10.5194/acp-18-15937-2018


shutdown. Geophysical Research Letters, 47(12), p.e2020GL088533. Cheng, Z., Luo, L., Wang, S., Wang, Y., Sharma, S., Shimadera, H., Wang, X., Bressi, M., Miranda, R.M.D., Jiang, J., Zhou, W., Fajardo, O., Yan, N., Hao, J., 2016. Status and characteristics of ambient PM2.5 pollution in global megacities. Environment International 89-90, 212–221. doi:10.1016/j.envint.2016.02.003 Chiacchio, M., Ewen, T., Wild, M., Chin, M., Diehl, T., 2011. Decadal variability of aerosol optical depth in Europe and its relationship to the temporal shift of the North Atlantic Oscillation in the realm of dimming and brightening. Journal of Geophysical Research 116. doi:10.1029/2010jd014471 Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R. and Feigin, V., 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), pp.1907-1918. Cooper, O.R., Parrish, D.D., Ziemke, J., Balashov, N.V., Cupeiro, M., Galbally, I.E., Gilge, S., Horowitz, L., Jensen, N.R., Lamarque, J.F. and Naik, V., 2014. Global distribution and trends of tropospheric ozone: An observation-based reviewGlobal distribution and trends of tropospheric ozone. Elementa: Science of the Anthropocene, 2. Dinku, T., 2019. Challenges with availability and quality of climate data in Africa. Extreme Hydrology and Climate Variability 71–80. doi:10.1016/b978-0-12-815998-9.00007-5 Dong, X., Fu, J.S., 2015. Understanding interannual variations of biomass burning from Peninsular Southeast Asia, part II: Variability and different influences in lower and higher atmosphere levels. Atmospheric Environment 115, 9–18. doi:10.1016/j.atmosenv.2015.05.052 Eeftens, M., Tsai, M.Y., Ampe, C., Anwander, B., Beelen, R., Bellander, T., Cesaroni, G., Cirach, M., Cyrys, J., de Hoogh, K. and De Nazelle, A., 2012. Spatial variation of PM2.5, PM10, PM2.5 absorbance and PMcoarse concentrations between and within 20 European study areas and the relationship with NO2–results of the ESCAPE project. Atmospheric Environment, 62, pp.303-317. Fadnavis S. et al. (2020) Atmospheric Aerosols and Trace Gases. In: Krishnan R., Sanjay J., Gnanaseelan C., Mujumdar M., Kulkarni A., Chakraborty S. (eds) Assessment of Climate Change over the Indian Region. Springer, Singapore. https://doi.org/10.1007/978-981-15-4327-2_5 Frost, G.J., Mckeen, S.A., Trainer, M., Ryerson, T.B., Neuman, J.A., Roberts, J.M., Swanson, A., Holloway, J.S., Sueper, D.T., Fortin, T., Parrish, D.D., Fehsenfeld, F.C., Flocke, F., Peckham, S.E., Grell, G.A., Kowal, D., Cartwright, J., Auerbach, N., Habermann, T., 2006. Effects of changing power plant NOx emissions on ozone in the eastern United States: Proof of concept. Journal of Geophysical Research 111. doi:10.1029/2005jd006354 Galloway, J.N., Dentener, F.J., Capone, D.G., Boyer, E.W., Howarth, R.W., Seitzinger, S.P., Asner, G.P., Cleveland, C.C., Green, P.A., Holland, E. and Karl, D.M., 2004.

https://doi.org/10.1007/978-981-15-4327-2_5

https://doi.org/10.1007/978-981-15-4327-2_5


Nitrogen cycles: past, present, and future. Biogeochemistry 70, 153e226. Georgoulias, A.K., A, R.J.V.D., Stammes, P., Boersma, K.F., Eskes, H.J., 2019. Trends and trend reversal detection in 2 decades of tropospheric NO<sub>2</sub> satellite observations. Atmospheric Chemistry and Physics 19, 6269–6294. doi:10.5194/acp-19-6269-2019 Ghude, S.D., Fadnavis, S., Beig, G., Polade, S.D., A, R.J.V.D., 2008. Detection of surface emission hot spots, trends, and seasonal cycle from satellite-retrieved NO2 over India. Journal of Geophysical Research 113. doi:10.1029/2007jd009615 Gill, S.S., Tuli, S., Xu, M., Singh, I., Singh, K.V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., Pervaiz, H., Sehgal, B., Kaila, S.S., Misra, S., Aslanpour, M.S., Mehta, H., Stankovski, V., Garraghan, P., 2019. Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things 8, 100118. doi:10.1016/j.iot.2019.100118 Guha, T., Mahata, S., Bhattacharya,S. K., Singh, B. B., Toyoda, S.,Yoshida, N., & Liang, M.‐C. (2020).Stratosphericincursion as a source ofenhancement of the isotopic ratios ofatmospheric N2O at Western Pacific.Earth and Space Science,7,e2020EA001102. https://doi.org/10.1029/2020EA001102 Gurjar, B., Butler, T., Lawrence, M., Lelieveld, J., 2008. Evaluation of emissions and air quality in megacities. Atmospheric Environment 42, 1593–1606. doi:10.1016/j.atmosenv.2007.10.048 Gurjar, B., Jain, A., Sharma, A., Agarwal, A., Gupta, P., Nagpure, A., Lelieveld, J., 2010. Human health risks in megacities due to air pollution. Atmospheric Environment 44, 4606–4613. doi:10.1016/j.atmosenv.2010.08.011 Gurjar, B., Ravindra, K., Nagpure, A.S., 2016. Air pollution trends over Indian megacities and their local-to-global implications. Atmospheric Environment 142, 475–495. doi:10.1016/j.atmosenv.2016.06.030 Hanumanthu, S., Vogel, B., Müller, R., Brunamonti, S., Fadnavis, S., Li, D., Ölsner, P., Naja, M., Singh, B. B., Kumar, K. R., Sonbawne, S., Jauhiainen, H., Vömel, H., Luo, B., Jorge, T., Wienhold, F. G., Dirkson, R., Peter, T., 2020. Strong day-to-day variability of the Asian Tropopause Aerosol Layer (ATAL) in August 2016 at the Himalayan foothills, Atmos. Chem. Phys., 20, 14273–14302, https://doi.org/10.5194/acp-20-14273-2020 He, G., Pan, Y., Tanaka, T., 2020. The short-term impacts of COVID-19 lockdown on urban air pollution in China. Nature Sustainability. doi:10.1038/s41893-020-0581-y Heft-Neal, S., Burney, J., Bendavid, E., Burke, M., 2018. Robust relationship between air quality and infant mortality in Africa. Nature 559, 254–258. doi:10.1038/s41586-018-0263-3 Hilboll, A., Richter, A., Burrows, J.P., 2013. Long-term changes of tropospheric NO2 over megacities derived from multiple satellite instruments. Atmospheric Chemistry and Physics 13, 4145–4169. doi:10.5194/acp-13-4145-2013 Holland, E.A., Dentener, F.J., Braswell, B.H., Sulzman, J.M., 1999. Contemporary and

https://doi.org/10.1029/2020EA001102

https://doi.org/10.5194/acp-20-14273-2020


pre-industrial global reactive nitrogen budgets. New Perspectives on Nitrogen Cycling in the Temperate and Tropical Americas 7–43. doi:10.1007/978-94-011-4645-6_2 Huang, Y., Zhou, J.L., Yu, Y., Mok, W.C., Lee, C.F. and Yam, Y.S., 2020. Uncertainty in the Impact of the COVID-19 Pandemic on Air Quality in Hong Kong, China. Atmosphere, 11(9), p.914. Jacob, D.J., Winner, D.A., 2009. Effect of climate change on air quality. Atmospheric Environment 43, 51–63. doi:10.1016/j.atmosenv.2008.09.051 Jamshidi, S., Baniasad, M. and Niyogi, D., 2020. Global to USA County Scale Analysis of Weather, Urban Density, Mobility, Homestay, and Mask Use on COVID-19. International journal of environmental research and public health, 17(21), p.7847. Kharol, S.K., Mclinden, C.A., Sioris, C.E., Shephard, M.W., Fioletov, V., Donkelaar, A.V., Philip, S., Martin, R.V., 2017. OMI satellite observations of decadal changes in ground-level sulfur dioxide over North America. Atmospheric Chemistry and Physics 17, 5921–5929. doi:10.5194/acp-17-5921-2017 Kim, N., Kim, Y., Kang, C.-H., 2011. Long-term trend of aerosol composition and direct radiative forcing due to aerosols over Gosan: TSP, PM10, and PM2.5 data between 1992 and 2008. Atmospheric Environment 45, 6107–6115. doi:10.1016/j.atmosenv.2011.08.051 Krecl, P., Targino, A.C., Landi, T.P., Ketzel, M., 2018. Determination of black carbon, PM2.5, particle number and NOx emission factors from roadside measurements and their implications for emission inventory development. Atmospheric Environment 186, 229–240. doi:10.1016/j.atmosenv.2018.05.042. Lalitaporn, P., Kurata, G., Matsuoka, Y., Thongboonchoo, N., Surapipith, V., 2013. Long-term analysis of NO2, CO, and AOD seasonal variability using satellite observations over Asia and intercomparison with emission inventories and model. Air Quality, Atmosphere & Health 6, 655–672. doi:10.1007/s11869-013-0205-z Lamsal, L.N., Martin, R.V., Donkelaar, A.V., Celarier, E.A., Bucsela, E.J., Boersma, K.F., Dirksen, R., Luo, C., Wang, Y., 2010. Indirect validation of tropospheric nitrogen dioxide retrieved from the OMI satellite instrument: Insight into the seasonal variation of nitrogen oxides at northern midlatitudes. Journal of Geophysical Research 115. Lelieveld, J., Dentener, F.J., 2000. What controls tropospheric ozone? Journal of Geophysical Research: Atmospheres 105, 3531–3551. doi:10.1029/1999jd901011 Li, J., Carlson, B.E., Lacis, A.A., 2015. How well do satellite AOD observations represent the spatial and temporal variability of PM 2.5 concentration for the United States? Atmospheric Environment 102, 260–273. doi:10.1016/j.atmosenv.2014.12.010 Li, L., Li, Q., Huang, L., Wang, Q., Zhu, A., Xu, J., Liu, Z., Li, H., Shi, L., Li, R., Azari, M., Wang, Y., Zhang, X., Liu, Z., Zhu, Y., Zhang, K., Xue, S., Ooi, M.C.G., Zhang, D., Chan, A., 2020. Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. Science of The Total Environment 732, 139282.


doi:10.1016/j.scitotenv.2020.139282 Lu, Z., Streets, D.G., Foy, B.D., Krotkov, N.A., 2013. Ozone Monitoring Instrument Observations of Interannual Increases in SO2 Emissions from Indian Coal-Fired Power Plants during 2005–2012. Environmental Science & Technology 47, 13993–14000. doi:10.1021/es4039648 Lu, Z., Zhang, Q., Streets, D.G., 2011. Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010. Atmospheric Chemistry and Physics 11, 9839–9864. doi:10.5194/acp-11-9839-2011 Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweerd, V., Gwynne, M., 1996. Urban air pollution in megacities of the world. Atmospheric Environment 30, 681–686. doi:10.1016/1352-2310(95)00219-7 Mahajan, A.S., Smedt, I.D., Biswas, M.S., Ghude, S., Fadnavis, S., Roy, C. and Roozendael, M.V. (2015). Inter-annual variations in satellite observations of nitrogen dioxide and formaldehyde over India. Atmos. Environ. 116: 194–201. Marlier, M.E., Jina, A.S., Kinney, P.L., Defries, R.S., 2016. Extreme Air Pollution in Global Megacities. Current Climate Change Reports 2, 15–27. doi:10.1007/s40641-016-0032-z Menut, L., Bessagnet, B., Siour, G., Mailler, S., Pennel, R., Cholakian, A., 2020. Impact of lockdown measures to combat Covid-19 on air quality over western Europe. Science of The Total Environment 741, 140426. doi:10.1016/j.scitotenv.2020.140426 Molina, M.J., Molina, L.T., 2004. Megacities and Atmospheric Pollution. Journal of the Air & Waste Management Association 54, 644–680. doi:10.1080/10473289.2004.10470936 Parrish, D.D., Zhu, T., 2009. Clean Air for Megacities. Science 326, 674–675. doi:10.1126/science.1176064 Muhammad, S., Long, X. and Salman, M., 2020. COVID-19 pandemic and environmental pollution: a blessing in disguise?. Science of The Total Environment, p.138820. Reisen, F., Meyer, C. M., Keywood, M.D., 2013. Impact of biomass burning sources on seasonal aerosol air quality. Atmospheric Environment 67, 437–447. doi:10.1016/j.atmosenv.2012.11.004 Rodríguez-Urrego, D., Rodríguez-Urrego, L., 2020. Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world. Environmental Pollution 266, 115042. doi:10.1016/j.envpol.2020.115042 Schaaf, C., Strahler, A., Gao, F., Lucht, W., Jin, Y., Li, X., Zhang, X., Tsvetsinskaya, E., Muller, J.P., Lewis, P. and Barnsley, M., 2002, June. Global albedo, BRDF and nadir BRDF-adjusted reflectance products from MODIS. In IEEE International Geoscience and Remote Sensing Symposium (Vol. 2, pp. 1188-1190). IEEE. Sharma, S., Zhang, M., Anshika, Gao, J., Zhang, H., Kota, S.H., 2020. Effect of restricted emissions during COVID-19 on air quality in India. Science of The Total Environment 728, 138878. doi:10.1016/j.scitotenv.2020.138878


Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., Skakun, S., 2017. Large scale crop classification using Google earth engine platform. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). doi:10.1109/igarss.2017.8127801 Shen, L., Zhao, T., Wang, H., Liu, J., Bai, Y., Kong, S., Zheng, H., Zhu, Y. and Shu, Z., 2020. Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China. Science of the Total Environment, 754, p.142227. Sidhu, N., Pebesma, E., Câmara, G., 2018. Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing 51, 486–500. doi:10.1080/22797254.2018.1451782 Singh, M., Krishnan, R., Goswami, B., Choudhury, A.D., Swapna, P., Vellore, R., Prajeesh, A.G., Sandeep, N., Venkataraman, C., Donner, R.V. and Marwan, N., 2020. Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling. Science advances, 6(38), p.eaba8164. doi:10.1126/sciadv.aba8164 Su, X., Goloub, P., Chiapello, I., Chen, H., Ducos, F., Li, Z., 2010. Aerosol variability over East Asia as seen by POLDER space-borne sensors. Journal of Geophysical Research: Atmospheres 115. doi:10.1029/2010jd014286 Solomon, S., Manning, M., Marquis, M. and Qin, D., 2007. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge university press. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., Brisco, B., 2020. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 164, 152–170. doi:10.1016/j.isprsjprs.2020.04.001 Tiwari, S., Srivastava, A., Bisht, D., Parmita, P., Srivastava, M.K., Attri, S., 2013. Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: Influence of meteorology. Atmospheric Research 125-126, 50–62. doi:10.1016/j.atmosres.2013.01.011 Tuli, S., Tuli, S., Tuli, R., Gill, S.S., 2020. Predicting the Growth and Trend of COVID-19 Pandemic using Machine Learning and Cloud Computing. Internet of Things, 11. doi:10.1101/2020.05.06.20091900 US Environmental Protection Agency, 2015. Technical Support Document: EPA’s 2011 National-scale Air Toxics Assessment. van der A, R.J., Mijling, B., Ding, J., Koukouli, M.E., Liu, F., Li, Q., Mao, H., Theys, N., 2017. Cleaning up the air: effectiveness of air quality policy for SO2 and NOx emissions in China. Veefkind, J., Aben, I., Mcmullan, K., Förster, H., Vries, J.D., Otter, G., Claas, J., Eskes, H., Haan, J.D., Kleipool, Q., Weele, M.V., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., Levelt, P., 2012. TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global


observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sensing of Environment 120, 70–83. doi:10.1016/j.rse.2011.09.027 Venter, Z.S., Aunan, K., Chowdhury, S., Lelieveld, J., 2020. COVID-19 lockdowns cause global air pollution declines with implications for public health risk. doi:10.1101/2020.04.10.20060673 Wang, J., Xing, J., Mathur, R., Pleim, J.E., Wang, S., Hogrefe, C., Gan, C.-M., Wong, D.C., Hao, J., 2017. Historical Trends in PM2.5-Related Premature Mortality during 1990–2010 across the Northern Hemisphere. Environmental Health Perspectives 125, 400–408. doi:10.1289/ehp298 World Urbanization Prospects 2018: Highlights, 2019. doi:10.18356/6255ead2-en World Health Organization, 2013. Review of evidence on health aspects of air pollution – REVIHAAP Project 309. World Health Organization, 2020. Weekly epidemiological update - 15 December 2020. https://www.who.int/publications/m/item/weekly-epidemiological-update---15-december-2020. Xu, K., Cui, K., Young, L.-H., Wang, Y.-F., Hsieh, Y.-K., Wan, S., Zhang, J., 2020. Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China. Aerosol and Air Quality Research 20, 1204–1221. doi:10.4209/aaqr.2020.04.0139

Documents

Quantifying COVID-19 enforced global changes in